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DOE OSTI2021

Per-Phase and 3-Phase Optimal Coordination of Directional Overcurrent Relays Using Genetic Algorithm

Penetration of the power grid by renewable energy sources, distributed storage, and distributed generators is becoming increasingly common. Increased utilization of these distributed energy resources (DERs) has given rise to additional protection coordination concerns, particularly where they are utilized in an unbalanced manner or where loading among phases is unbalanced. Digital relays such as the SEL-751 (produced by Schweitzer Engineering Laboratories, Pullman, WA, USA) series have the capability of being set on a per-phase basis. This capability is underutilized in common practice. Additionally, in optimization algorithms for determining relay settings, the time-overcurrent characteristics (TOCs) of relays are generally not treated as variables and are assigned before running the optimization algorithm. In this paper, TOC options themselves are treated as discrete variables to be considered in the optimization algorithm. A mixed integer nonlinear programming problem (MINLP) is set up where the goal is to minimize relay operating times. A genetic algorithm (GA) approach is implemented in MATLAB where two cases are considered. In the first case, the TOC and Time dial setting (TDS) of each relay is set on a three-phase basis. In the second case, per-phase settings are considered. Relay TDSs and TOCs are both considered as simultaneous discrete control variables. Despite the stochastic nature of using per-phase settings for unbalanced systems is found to generally allow for shorter operating times. However, for relatively balanced systems, it is best to use three-phase settings if computation time is of importance.

Matthews, Ronald C.↗

DOE OSTI2020

Model for Collaboration among Carriers to Reduce Empty Container Truck Trips

In recent years, intermodal transport has become an increasingly attractive alternative to freight shippers. However, the current intermodal freight transport is not as efficient as it could be. Oftentimes an empty container needs to be transported from the empty container depot to the shipper, and conversely, an empty container needs to be transported from the receiver to the empty container depot. These empty container movements decrease the freight carrier’s profit, as well as increase traffic congestion, decrease roadway safety, and add unnecessary emissions to the environment. To this end, our study evaluates a potential collaboration strategy to be used by carriers for domestic intermodal freight transport based on an optimization approach to reduce the number of empty container trips. A binary integer-linear programming model is developed to determine each freight carrier’s optimal schedule while minimizing its operating cost. The model ensures that the cost for each carrier with collaboration is less than or equal to its cost without collaboration. It also ensures that average savings from the collaboration are shared equally among all participating carriers. Additionally, two stochastic models are provided to account for uncertainty in truck travel times. The proposed collaboration strategy is tested using empirical data and is demonstrated to be effective in meeting all of the shipment constraints.

42 ENGINEERING↗

DOE OSTI2020

Cooperative Load Scheduling for Multiple Aggregators Using Hierarchical ADMM

Demand response (DR) serves an important role in improving the efficiency and stability of power systems. In recent years, with advances in communication and smart device technologies, many aggregators have emerged to facilitate end customer participation in DR programs. These aggregators, equipped with customized optimal control algorithms, are capable of providing various grid services. Among them is load scheduling during DR events, namely following a load signal provided by the utility company while minimizing overall customer discomfort. However, as the number of aggregators keeps increasing, it becomes challenging for utility companies to conduct load scheduling for multiple aggregators and generate reference signals for each of them. This paper proposes an optimization framework using hierarchical alternating direction method of multipliers (H-ADMM) to optimally generate load following signals for multiple aggregators. Under this framework, utility and multiple aggregators work in a cooperative manner, aiming at minimizing an overall system cost from different levels of the power system hierarchy, while protecting user privacy. A case study has been conducted in a system with multiple aggregators, based on control of HVAC loads. Experimental results validate the effectiveness of the proposed algorithm.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Cooperative Load Scheduling for Multiple Aggregators Using Hierarchical ADMM: Preprint

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Fuel Properties and Kinetics

The Co-Optimization of Fuels and Engines (CoOptima) program is focused on a two-pronged approach to develop new, lower net-carbon fuels and advanced engine technologies in tandem. This effort brings nine national labs and several university and industry partners together to simultaneously identify new fuel candidates and to further engine technology development in the light-, medium-, and heavy-duty vehicle sectors with a focus on fuel economy and emission reduction. The fuel properties and kinetics project is aimed at achieving a better understanding of fuel property kinetics and how they impact engine performance such that more efficient and lower emitting engines can be made a reality. Several promising biofuel candidates were identified through tiered screening approaches and were evaluated utilizing unique bench-scale measurement techniques. Kinetic models were developed to describe autoignition and soot pre-cursor formation mechanisms to identify the most promising candidates that advanced various combustion strategies.

ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2018

International Good Practice in Preparing for Decommissioning - 18404

The transition of a nuclear facility from operation to the implementation of dismantling is a critical phase in every decommissioning project. Several organizational and technical modifications are needed to adapt the facility to meet the new objectives and requirements. A variety of activities need to be planned and performed to support the transition and to prepare the dismantling of the facility. Experience has shown that it is essential to start with preparations for decommissioning at a very early stage, at best already during the design stage of the facility but at least during the operational stage. The preparation of the transition for decommissioning and dismantling (D and D) is a key issue for the success of the global D and D project to minimize delays and undue costs; to optimize personnel and other resources; and to initiate preparatory activities for decommissioning in a planned, timely and cost-effective manner and thus to ensure safe and efficient decommissioning. With the growing number of nuclear facilities reaching the decommissioning stage, the Working Party on Decommissioning and Dismantling (WPDD) within the Nuclear Energy Agency of OECD formed the Task Group on Preparing for Decommissioning. This group of experts include regulators, nuclear operators and independent experts to review the strategic aspects to optimize the preparations for decommissioning from the last years of operation onwards. This paper gives and overview of the work carried out by TGPFD between 2015 and 2017 and covers strategic approaches, issues and risks and observations of good practice rather than providing detailed descriptions of methods or technologies. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2018

Process Control Approach to Implement High Waste Loading Glass Formulations for Hanford Low Activity Waste Vitrification - 18435

Acceptable glass formulations for vitrification of Hanford low activity waste (LAW) at The Hanford Tank Waste Treatment and Immobilization Plant (WTP) must meet a variety of product quality, processability, and waste loading requirements. The glass formulation development work underpinning the WTP baseline LAW glasses was captured in a vitrification process control approach developed by the Vitreous State Laboratory (VSL) and Atkins Energy Federal EPC, Inc. that permits the calculation of the glass formulation required for each particular batch of LAW delivered to the LAW vitrification facility. Since that time, we have developed a wide range of glass formulations that achieve considerably higher waste loadings and which therefore are more economically attractive in terms of reduced amounts of glass produced and decreased time for treatment. That work was done in support of the Department of Energy's Office of River Protection program that is examining options to optimize the LAW facility and LAW glass waste form. Implementation of these high waste loading formulations at the WTP requires the development of a new LAW vitrification process control algorithm, which is the subject of this paper. Waste loadings in LAW glasses are typically limited by either the alkali loading for LAW with low sulfate content or the SO{sub 3} loading for LAW with high sulfate content. In the former case, waste loadings are limited by refractory corrosion and glass corrosion on the vapor hydration test (VHT) and product consistency test (PCT), all of which tend to increase with higher alkali concentrations. In the latter case, waste loadings are limited by the formation of a molten sulfate phase in the melter, which has deleterious effects. The LAW vitrification process control strategy must mitigate these effects while optimizing waste loadings. Given the composition of any LAW stream, the new LAW vitrification process control algorithm permits the calculation of a suitable high waste loading glass composition and the required glass forming additives such that all processing, waste loading, and product quality requirements are met. This algorithm was developed from data for a large number of glasses that were formulated, and characterized with respect to a variety of properties affecting processability and product quality; many of these formulations were also subjected to melter testing to verify acceptable processing characteristics, processing rates, and the absence of secondary sulfate phase formation. This paper describes the development of the present algorithm, comparisons to the present lower waste loading baseline WTP process control approach, and plans for further development and testing. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2018

Remediation of a 3-Mile Hexavalent Chromium Plume in Hinkley, California - 18532

Pacific Gas and Electric Company (PG and E) is conducting assessment and cleanup of hexavalent chromium (Cr[VI]) in groundwater related to industrial activities from the 1950's and 1960's at the PG and E Hinkley Compressor Station near Barstow, California (the Site). The plume core undergoing remediation is approximately 4.8 kilometers long and 1.6 kilometers wide (3 miles long and one mile wide). In December 2015, a Cleanup and Abatement Order was issued which established cleanup requirements for the Site, including required cleanup time frames for the plume core undergoing remediation to reach 50 micrograms per liter (μg/L) across 90% of the plume by 2025 and to reach 10 μg/L across 80% of the plume by 2032. To reach the milestones at the scale of the site, remedy design includes a series of remedial system expansions and remedy implementation relies on an adaptive management scheme for continual performance evaluation and optimization. This presentation will detail the remedial approach, implementation and performance to date. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2018

Fit for Purpose Remote Technology Decommissioning Solutions - 18630

The Magnox Decommissioning Project is actively progressing clean-up and decommissioning of 12 reactor sites across England, Scotland and Wales. Funded and directed by the United Kingdom's Nuclear Decommissioning Authority (NDA) and managed by the Cavendish Fluor Partnership (CFP), the Magnox Decommissioning Project introduces a wide range of radiological and industrial hazards warranting deployment of remote technologies to safely de-inventory, decontaminate and decommission a vast array of nuclear facilities. The Magnox Decommissioning Project encompasses 22 former Magnox nuclear power production reactors across 10 reactor sites and five research/test reactors at two former research sites. Decommissioning activities have been underway at some sites for nearly 20 years with early focus on retrieving and stabilising wastes and draining and stabilising spent fuel ponds. The Magnox decommissioning strategy invokes a programme perspective to drive consistency in tackling similar decommissioning and waste challenges across multiple sites. Programme central teams have worked with technology suppliers to foster learning from industry experience to develop and deploy tooling and robotics. The programme approach has also facilitated learning from one site to the next. In many cases, this central programme approach has promoted an evolution of technologies resulting in optimisation of simple remote tooling. In other instances, collaboration with suppliers is producing state-of-the-art technologies to tackle more unique challenges including, most recently, early demonstration of laser cutting for reactor vessel dismantling. This paper will outline the programme management approach and the scalable optioneering process Magnox employs to select the engineered controls and technologies used in decommissioning and waste management activities. It will provide an overview of the broad range of remote tooling and robotics deployed across the 12 Magnox sites by exploring some specific examples. Lastly, the paper will summarise key lessons learned in the now extensive history of selecting and using remote technologies to decommission Magnox nuclear facilities. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2017

Human Factors Principles in Information Dashboard Design - 278

Refueling outages remain one of the largest opportunities for improving capacity factor and reducing costs available to commercial nuclear power plants. Although the nuclear industry has made steady improvement in outage optimization, each day of a refueling outage still represents an opportunity to save millions of dollars and each day an outage extends past its planned end date represents millions of dollars that may have been spent unnecessarily. Reducing planned outage duration or preventing outage extensions requires careful management of the outage schedule as well as constant oversight and monitoring of work completion during the outage execution. During a typical outage, there are typically more than 10,000 activities on the schedule that, if not managed efficiently, may cause expensive outage delays. Management of outages currently relies largely on paper-based resources and general-purpose office software. A typical method currently used to monitor work performance is a burn-down curve, where total remaining activities are plotted against the baseline schedule to track bulk work completion progress. While methods like this are useful, there is still considerable uncertainty during a typical outage whether bulk work progress is adequate and therefore a lot of management time is spent analyzing the situation on a daily basis. This paper is a case study of how new technology, in combination with established human-factors and user-centered design knowledge, can be used to support Outage Control Center (OCC) personnel decision making. In particular, we describe recent advances made in developing a framework for the design of visual outage information presentation, as well as an overview of the human factors principles that informed the development of the visualizations. To test the utility of advanced visual outage information presentation, an outage management dashboard software application was created as part of the Department of Energy's Advanced Outage Control Center project (AOCC). This dashboard is intended to present all the critical information an outage manager would need to understand the current status of a refueling outage. The dashboard presents the critical path, bulk work performance, key performance indicators, outage milestones and metrics relating current performance to historical performance. Additionally, the dashboard includes data analysis tools to allow outage managers to drill down into the underlying data to understand the drivers of the indicators. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2024

Using Likwid and Byfl to Benchmark Hardware Performance

This paper outlines a benchmarking study conducted during my internship at LANL, focusing on CPU (Computer Processing Unit) and program performance assessment. The primary goal was to gather memory access data using three methods across five polybench kernels The data gathered would then be used to compare and contrast to one another and calculate operational intensity for performance comparisons. Benchmarking tools like Byfl and Likwid were employed, with Byfl offering hardware-independent data through LLVM compiler communication and Likwid directly interacting with computer hardware. The study considered various benchmarking factors, including optimization levels, Big O notation ((n)), CPU diversity and specific kernel equations. Big O notation was utilized to simplify code complexity, with detailed breakdwons of operations and memory components for each polybench application. Specific O(n) equations enabled nuanced kernel compariosns, facilitating the identification of performance variations. CPU efficiency assessments were conducted using Likwid tests on two CPUs. The central focus on code optimization aimed at achieving higher speeds and reduced memory usage through streamlined code. Future work propsoes creating a roofline model, synthesizing benchmarking data into a comprehensive data graph to assist in optimizing code and improving hardware performance. The potential impact on the laboratory or national mission was underscored, emphasizing the importance of optimizing applications and hardware to conserve resources and accelerate program execution. The specific relevance to LANL’s operations in math-intensive fields such as Nuclear Fission, Space Exploration, and Nanotechnology highlights the necessity of efficient benchmarking for resource conservation and proram speed. Overall, this study contributes to the understanding of CPU and program performance, providing insights for future optimization efforts in a laboratory setting

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Actinide Mixing Optimization Solver

The Actinide Mixing Optimization Solver (AMOS) is a set of programs written in MATLAB that computes theoretical mixes of different uranium, plutonium, thorium, and other actinide materials given a list of isotopic reference materials. Constraints are applied depending on the desired product and a mixture is computed to meet the specified requirements. The optimal mixture consists of a product that uses the lowest possible number of initial actinide materials and has the lowest cost. AMOS is currently applied to particular reference material requests from the sponsoring agency (NNSA) on behalf of the IAEA.

Baldwin, Aaron Taylor↗

DOE OSTI2015

Mean-Variance Problems for Finite Horizon Semi-Markov Decision Processes

This paper deals with a mean-variance problem for finite horizon semi-Markov decision processes. The state and action spaces are Borel spaces, while the reward function may be unbounded. The goal is to seek an optimal policy with minimal finite horizon reward variance over the set of policies with a given mean. Using the theory of N-step contraction, we give a characterization of policies with a given mean and convert the second order moment of the finite horizon reward to a mean of an infinite horizon reward/cost generated by a discrete-time Markov decision processes (MDP) with a two dimension state space and a new one-step reward/cost under suitable conditions. We then establish the optimality equation and the existence of mean-variance optimal policies by employing the existing results of discrete-time MDPs. We also provide a value iteration and a policy improvement algorithms for computing the value function and mean-variance optimal policies, respectively. In addition, a linear program and the dual program are developed for solving the mean-variance problem.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2015

Cost-Optimal Pathways to 75% Fuel Reduction in Remote Alaskan Villages: Preprint

There are thousands of isolated, diesel-powered microgrids that deliver energy to remote communities around the world at very high energy costs. The Remote Communities Renewable Energy program aims to help these communities reduce their fuel consumption and lower their energy costs through the use of high penetration renewable energy. As part of this program, the REopt modeling platform for energy system integration and optimization was used to analyze cost-optimal pathways toward achieving a combined 75% reduction in diesel fuel and fuel oil consumption in a select Alaskan village. In addition to the existing diesel generator and fuel oil heating technologies, the model was able to select from among wind, battery storage, and dispatchable electric heaters to meet the electrical and thermal loads. The model results indicate that while 75% fuel reduction appears to be technically feasible it may not be economically viable at this time. When the fuel reduction target was relaxed, the results indicate that by installing high-penetration renewable energy, the community could lower their energy costs by 21% while still reducing their fuel consumption by 54%.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2014

Modeling and optimizing of the random atomic spin gyroscope drift based on the atomic spin gyroscope

In order to improve the atom spin gyroscope's operational accuracy and compensate the random error caused by the nonlinear and weak-stability characteristic of the random atomic spin gyroscope (ASG) drift, the hybrid random drift error model based on autoregressive (AR) and genetic programming (GP) + genetic algorithm (GA) technique is established. The time series of random ASG drift is taken as the study object. The time series of random ASG drift is acquired by analyzing and preprocessing the measured data of ASG. The linear section model is established based on AR technique. After that, the nonlinear section model is built based on GP technique and GA is used to optimize the coefficients of the mathematic expression acquired by GP in order to obtain a more accurate model. The simulation result indicates that this hybrid model can effectively reflect the characteristics of the ASG's random drift. The square error of the ASG's random drift is reduced by 92.40%. Comparing with the AR technique and the GP + GA technique, the random drift is reduced by 9.34% and 5.06%, respectively. The hybrid modeling method can effectively compensate the ASG's random drift and improve the stability of the system.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2011

GPU COMPUTING FOR PARTICLE TRACKING

This is a feasibility study of using a modern Graphics Processing Unit (GPU) to parallelize the accelerator particle tracking code. To demonstrate the massive parallelization features provided by GPU computing, a simplified TracyGPU program is developed for dynamic aperture calculation. Performances, issues, and challenges from introducing GPU are also discussed. General purpose Computation on Graphics Processing Units (GPGPU) bring massive parallel computing capabilities to numerical calculation. However, the unique architecture of GPU requires a comprehensive understanding of the hardware and programming model to be able to well optimize existing applications. In the field of accelerator physics, the dynamic aperture calculation of a storage ring, which is often the most time consuming part of the accelerator modeling and simulation, can benefit from GPU due to its embarrassingly parallel feature, which fits well with the GPU programming model. In this paper, we use the Tesla C2050 GPU which consists of 14 multi-processois (MP) with 32 cores on each MP, therefore a total of 448 cores, to host thousands ot threads dynamically. Thread is a logical execution unit of the program on GPU. In the GPU programming model, threads are grouped into a collection of blocks Within each block, multiple threads share the same code, and up to 48 KB of shared memory. Multiple thread blocks form a grid, which is executed as a GPU kernel. A simplified code that is a subset of Tracy++ [2] is developed to demonstrate the possibility of using GPU to speed up the dynamic aperture calculation by having each thread track a particle.

42 ENGINEERING↗

DOE OSTI2015

TU-EF-204-09: A Preliminary Method of Risk-Informed Optimization of Tube Current Modulation for Dose Reduction in CT

Purpose: X-rays from CT scans can increase cancer risk to patients. Lifetime Attributable Risk of Cancer Incidence for adult patients has been investigated and shown to decrease as patient age. However, a new risk model shows an increasing risk trend for several radiosensitive organs for middle age patients. This study investigates the feasibility of a general method for optimizing tube current modulation (TCM) functions to minimize risk by reducing radiation dose to radiosensitive organs of patients. Methods: Organ-based TCM has been investigated in literature for eye lens dose and breast dose. Adopting the concept in organ-based TCM, this study seeks to find an optimized tube current for minimal total risk to breasts and lungs by reducing dose to these organs. The contributions of each CT view to organ dose are determined through simulations of CT scan view-by-view using a GPU-based fast Monte Carlo code, ARCHER. A Linear Programming problem is established for tube current optimization, with Monte Carlo results as weighting factors at each view. A pre-determined dose is used as upper dose boundary, and tube current of each view is optimized to minimize the total risk. Results: An optimized tube current is found to minimize the total risk of lungs and breasts: compared to fixed current, the risk is reduced by 13%, with breast dose reduced by 38% and lung dose reduced by 7%. The average tube current is maintained during optimization to maintain image quality. In addition, dose to other organs in chest region is slightly affected, with relative change in dose smaller than 10%. Conclusion: Optimized tube current plans can be generated to minimize cancer risk to lungs and breasts while maintaining image quality. In the future, various risk models and greater number of projections per rotation will be simulated on phantoms of different gender and age. National Institutes of Health R01EB015478.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2011

Optimally Controlled Flexible Fuel Powertrain System

A multi phase program was undertaken with the stated goal of using advanced design and development tools to create a unique combination of existing technologies to create a powertrain system specification that allowed minimal increase of volumetric fuel consumption when operating on E85 relative to gasoline. Although on an energy basis gasoline / ethanol blends typically return similar fuel economy to straight gasoline, because of its lower energy density (gasoline ~ 31.8MJ/l and ethanol ~ 21.1MJ/l) the volume based fuel economy of gasoline / ethanol blends are typically considerably worse. This project was able to define an initial engine specification envelope, develop specific hardware for the application, and test that hardware in both single and multi-cylinder test engines to verify the ability of the specified powertrain to deliver reduced E85 fuel consumption. Finally, the results from the engine testing were used in a vehicle drive cycle analysis tool to define a final vehicle level fuel economy result. During the course of the project, it was identified that the technologies utilized to improve fuel economy on E85 also enabled improved fuel economy when operating on gasoline. However, the E85 fueled powertrain provided improved vehicle performance when compared to the gasoline fueled powertrain due to the improved high load performance of the E85 fuel. Relative to the baseline comparator engine and considering current market fuels, the volumetric fuel consumption penalty when running on E85 with the fully optimized project powertrain specification was reduced significantly. This result shows that alternative fuels can be utilized in high percentages while maintaining or improving vehicle performance and with minimal or positive impact on total cost of ownership to the end consumer. The justification for this project was two-fold. In order to reduce the US dependence on crude oil, much of which is imported, the US Environmental Protection Agency (EPA) developed the Renewable Fuels Standard (RFS) under the Energy Policy Act of 2005. The RFS specifies targets for the amount of renewable fuel to be blended into petroleum based transportation fuels. The goal is to blend 36 billion gallons of renewable fuels into transportation fuels by 2022 (9 billion gallons were blended in 2008). The RFS also requires that the renewable fuels emit fewer greenhouse gasses than the petroleum fuels replaced. Thus the goal of the EPA is to have a more fuel efficient national fleet, less dependent on petroleum based fuels. The limit to the implementation of certain technologies employed was the requirement to run the developed powertrain on gasoline with minimal performance degradation. The addition of ethanol to gasoline fuels improves the fuels octane rating and increases the fuels evaporative cooling. Both of these fuel property enhancements make gasoline / ethanol blends more suitable than straight gasoline for use in downsized engines or engines with increased compression ratio. The use of engine downsizing and high compression ratios as well as direct injection (DI), dual independent cam phasing, external EGR, and downspeeding were fundamental to the fuel economy improvements targeted in this project. The developed powertrain specification utilized the MAHLE DI3 gasoline downsizing research engine. It was a turbocharged, intercooled, DI engine with dual independent cam phasing utilizing a compression ratio of 11.25 : 1 and a 15% reduction in final drive ratio. When compared to a gasoline fuelled 2.2L Ecotec engine in a Chevrolet HHR, vehicle drive cycle predictions indicate that the optimized powertrain operating on E85 would result in a reduced volume based drive cycle fuel economy penalty of 6% compared to an approximately 30% penalty for current technology engines.

33 ADVANCED PROPULSION SYSTEMS↗